Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv Machine Learning

Jul 21

A Framework for Early Sepsis Prediction via Self-Supervised (JEPA) and Federated Representation Learning

arXiv:2607. 16681v1 Announce Type: new Abstract: Early sepsis prediction from electronic health records is challenged by irregular sampling, high missingness, and class imbalance.

By Umair bin Mansoor, Munaf Rashid, Roomi Naqvi

rag fine-tuning efficiency benchmarks

arXiv AI

Jul 21

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

arXiv:2507. 20804v3 Announce Type: replace Abstract: Large Language Models (LLMs) suffer from hallucinations due to their static parametric knowledge.

By Xueyao Wan, Hang Yu

llms rag multimodal benchmarks safety

arXiv AI

Jul 21

Phantom Transitions in Language Model Fine-Tuning: A Density-Matrix Analysis

arXiv:2606. 07559v2 Announce Type: replace-cross Abstract: Fine-tuning a language model often fails silently when its correct completion must outrank a near-synonym competitor.

By Vaibhav Prakash, Jayasri Dontabhaktuni

llms rag fine-tuning

arXiv AI

Jul 21

From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development

arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.

By Muzhi Li, Jinhu Qi, Yihong Wu, Minghao Zhao, Liheng Ma, Yifan Li, Xinyu Wang, Zhenghan Tai, Zixing Song, Yingxue Zhang, Ho-fung Leung, Irwin King

llms rag agents nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

A Survey on Knowledge-Oriented Retrieval-Augmented Generation

arXiv:2503. 10677v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has gained significant attention in recent years for its potential to enhance natural language understanding and generation by combining large-scale retrieval systems with generative models.

By Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang, Enhong Chen

rag nlp multimodal benchmarks safety

arXiv Machine Learning

Jul 21

Vector Search As Nearest Neighbor Matching: RAG-based Policy Learning in Causal Inference

arXiv:2607. 18225v1 Announce Type: cross Abstract: We propose one-step and two-step methods for policy learning with retrieval-augmented generation (RAG).

By Masahiro Kato, Taka Kato

llms rag

arXiv AI

Jul 21

SAGA: Synthetic Agentic Graph Architecture for Temporal Benchmark Generation

arXiv:2607. 17288v1 Announce Type: cross Abstract: High quality temporal graph benchmarks with rich semantics and ground-truth anomaly labels are essential for training graph neural networks, yet remain scarce due to privacy constraints and annotation costs.

By Jiacheng Ding, Xiaofei Zhang

llms rag agents benchmarks safety

arXiv AI

Jul 21

TurboVec: A Case Study in Cost-Efficient Private Retrieval for Enterprise RAG via Codebook-Oblivious Quantization

arXiv:2607. 16973v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems increasingly power enterprise LLM applications, yet the vector retrieval layer introduces two underexplored challenges: (1) trained codebook quantizers may expose corpus statistics during index construction, creating a leakage channel in multi-tenant deployments, and (2) post-hoc filtering for tenant isolation degrades recall on selective queries.

By Navnit Shukla, Kamal Pandey, Omsankar Tiwari

llms rag efficiency benchmarks safety

arXiv AI

Jul 21

Anticipate Before Acting: Future-State-Conditioned Vision-Language Navigation

arXiv:2607. 18042v1 Announce Type: cross Abstract: End-to-end vision-language navigation (VLN) with causal vision-language models can map instructions and egocentric observations directly to actions, but standard behavior cloning supervises only the next action and does not explicitly train the policy state to be predictive of future visual outcomes.

By Lingfeng Zhang, Zhanguang Zhang, Liheng Ma, Tongtong Cao, Yingxue Zhang

llms rag multimodal

arXiv AI

Jul 21

Kernelized Linear Attention: Breaking the Capacity Wall with Symmetric Cones

arXiv:2607. 17419v1 Announce Type: cross Abstract: Linear attention promises constant-time recurrent inference but degrades sharply on associative recall.

By Ayoub Ghriss, Sourav Chakraborty

llms rag efficiency benchmarks

arXiv AI

Jul 21

Memory in the Loop: In-Process Retrieval as Extended Working Memory for Language Agents

arXiv:2607. 05690v2 Announce Type: replace Abstract: Language agents run a loop - observe, reason, act - but the memory they reason over sits outside it: a store queried at most once per turn.

By Yusuf Khan, Carlo Lipizzi

llms rag agents

arXiv AI

Jul 21

Training Continuous Chain of Thought Models: A Tale of Two Regimes

arXiv:2607. 16972v1 Announce Type: new Abstract: Continuous Chain-of-Thought methods replace verbose reasoning traces with a short sequence of dense latent representations.

By Varun Yerram, He He, Eunsol Choi

rag

arXiv AI

Jul 21

Beyond Memory Leaderboards: Evaluating Scientific Memory as Budgeted Context Restoration

arXiv:2607. 16848v1 Announce Type: cross Abstract: Long-term memory is becoming a core component of LLM agents, but most memory benchmarks evaluate conversations or compact summaries, while research agents need to restore evidence from full scientific papers.

By Maksim Sheverev, David Finkelstein, Sergey Nikolenko

llms rag agents benchmarks

arXiv AI

Jul 21

Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition

arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.

By Mohd Halim Mohd Noor, Abdulrahman M. A. Baraka

rag benchmarks

arXiv Machine Learning

Jul 21

fSRD: Fuzzy Spectral Region Decomposition -- Automated Multi Operator Koopman Representations via an Adaptive Spectral Learning Architecture

arXiv:2607. 17990v1 Announce Type: new Abstract: Highly nonlinear chaotic dynamical systems remain difficult to model due to fundamental trade-offs between complexity, expressivity, and data efficiency.

By Charles Bokor, Mark Cary, Denise Morrey, Fabrizio Bonatesta

rag safety

arXiv AI

Jul 21

Retrieval is Enough: Training-Free Interpretability with a Tool-Using Agent

arXiv:2607. 16448v1 Announce Type: cross Abstract: Interpretability methods for neural network activations span a wide cost spectrum, from cheap, training-free techniques (such as linear probes, PCA, SVD) to more expensive training-based ones (such as SAEs and activation oracles).

By Sriram Balasubramanian, Soheil Feizi

llms rag agents diffusion benchmarks safety

arXiv AI

Jul 21

Semantically Similar, Logically Distinct: Diagnosing the Semantic-Answerability Gap in Table RAG

arXiv:2607. 17742v1 Announce Type: new Abstract: Tables are a critical knowledge source in retrieval-augmented generation (RAG), but a retrieved table may lack sufficient evidence to answer a query, a property we call answerability.

By Jiaming Tian, Liyao Li, Wentao Ye, Haobo Wang, Lihua Yu, Zujie Ren, Gang Chen, Junbo Zhao

rag benchmarks

arXiv AI

Jul 21

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering

arXiv:2604. 09757v2 Announce Type: replace-cross Abstract: Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded once as static context, and subsequent inference is dominated by language.

By Suyang Xi, Songtao Hu, Yuxiang Lai, Wangyun Dan, Yaqi Liu, Shansong Wang, Xiaofeng Yang

llms rag nlp fine-tuning benchmarks

arXiv AI

Jul 21

PGN: Design and Implementation of a Vision-Language Navigation System Based on Pangu Multimodal Foundation Model

arXiv:2607. 17806v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an embodied agent to interpret a natural-language instruction and predict actions from temporally ordered visual observations.

By Li Xian, Mingxi Li, Yizheng Wang, Yiming Shen, Qi Chen, Zhuoling Xiao

llms rag agents robotics fine-tuning multimodal safety

arXiv Machine Learning

Jul 21

Taurus: Accelerating Out-of-Core Graph Neural Network Inference on Billion-Scale Graphs

arXiv:2607. 17374v1 Announce Type: cross Abstract: Graph Neural Network (GNN) inference on billion-scale graphs is challenging due to the large memory footprint of features and embeddings and high disk I/O costs in out-of-core settings.

By Pranjal Naman, Yogesh Simmhan

rag efficiency

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